Processing facilities are often managed using process control systems. Example processing facilities include manufacturing plants, chemical plants, crude oil refineries, ore processing plants, and paper or pulp manufacturing and processing plants. Among other operations, process control systems typically manage the use of motors, valves, and other industrial equipment in the processing facilities.
In conventional process control systems, controllers are often used to control the operation of the industrial equipment in the processing facilities. The controllers could, for example, monitor the operation of the industrial equipment, provide control signals to the industrial equipment, and generate alarms when malfunctions are detected.
Model predictive control (MPC) is a widely-accepted control technique and is used throughout many industries. Currently, linear models are used by the vast majority of installed commercial MPC applications. There is, however, a growing recognition of the benefits of using nonlinear modeling and control. This realization has historically been tempered, largely due to increased demands on plant operations and the need (real or perceived) for highly-skilled personnel.
Although process industries at large are only beginning to pursue nonlinear techniques, the academic community has been engaged in a considerable amount of research in this area over the last few decades. For convenience, this research can be categorized into two broad approaches. One approach has focused on the use of “first principle” models, and the other approach has focused on the use of “empirical-based” models.
Early versions of nonlinear control based on first principle models were cast as a nonlinear programming (NLP) problem, where nonlinear programming was used to solve a set of differential algebraic equations (DAE). Typically, differential algebraic equations were derived by applying mass, energy, and momentum conservation equations to a system of interest. However, this was usually a very time-consuming process, required highly-skilled resources, and required continuous model updating. More recently, this approach has been extended to include discrete events. Problems of this class can be referred to as mixed-integer NLP (MINLP) and are receiving significant academic attention. While these and similar approaches have been successful in some areas (such as the polymer industry), they have not had wide acceptance due to their complexity.